Integrated Microfluidic Device for Mass Spectrometry-Based Proteomics and Its Application to Biomarker Discovery Programs
Bibliographic record
Abstract
The present investigation describes the analytical performances of a microfluidic device comprising an enrichment column, a reversed-phase separation channel, and a nanoelectrospray emitter embedded altogether in polyimide layers. This configuration minimizes transfer lines and connections and reduces postcolumn peak broadening and dead volumes. This compact and versatile modular nanoLC-chip system was interfaced to both ion trap and time-of-flight mass spectrometers, and its analytical potentials were evaluated in the context of proteomics applications. The figures of merit of this system in terms of peak capacity, reproducibility, sensitivity, and linear dynamic range of peptide detection were determined using tryptic digests of complex protein extracts including albumin- and immunoglobulin-depleted rat plasma samples. The analysis of peak profiles for more than 600 peptide ions reproducibly detected across replicate nanoLC-chip-MS runs (n = 10) indicated that this system provided good reproducibility of retention time and peak intensity with RSD values of less than 0.5 and 9.1%, respectively. Variation in peptide abundance as low as 2-fold changes was identified for spiked tryptic digests present at levels of 2-5 fmol in plasma samples. Sensitivity measurements were performed on dilution series of protein digests spiked into rat plasma samples and provided a detection limit of 1-5 fmol. The modular concept of the microfluidic systems also facilitated the integration of two-dimensional chromatography (strong cation exchange/C18) thereby increasing the sample loading and selectivity of the nanoLC-chip-MS system. The application of this integrated device was evaluated for complex rat plasma samples to compare the number of protein identifications obtained using one- and two-dimensional nanoLC-chip-MS/MS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".